Deep Convolution Neural Networks, or Deep CNN is the most core of the remarkable development in the field of Deep Learning. Though the CNN has been employed to solve character recognition problems in 1990s, it is not until recently that the CNN has become widespread in Machine Learning. For example, in 2012, the CNN significantly outperformed its competitors in an annual software contest, the ImageNet Large Scale Visual Recognition Challenge, and won the contest. After that, the CNN has become a very useful tool in the field of machine learning.
Meanwhile, image segmentation is a method of generating a label image using an input image, e.g., a training image, a test image. Recently, the Deep Learning has been so much widespread that the Deep Learning is being applied to the image segmentation.
When learning parameters of the CNN for the image segmentation, basically all areas in the image are learned with a same weight. However, in case at least one specific area, corresponding to at least one important region, in the image is a small area, the number of pixels included in the important region is also small. Accordingly, errors corresponding to the important region are reflected less on losses so that the important region cannot be learned properly.